BioGeoBEARS analyses using MrBayes MCC trees

Setup workspace with all the libraries and ammendments we’ll need

### Clear the workspace
rm(list=ls())
### load the libraries
library(BioGeoBEARS)
library(optimx)
library(FD)       # for FD::maxent() (make sure this is up-to-date)

Loading required package: ade4
Loading required package: geometry
Loading required package: magic
Loading required package: abind
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3

Attaching package: ‘vegan’

The following object is masked from ‘package:ade4’:

    cca

library(snow)
library(parallel)

Attaching package: ‘parallel’

The following objects are masked from ‘package:snow’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, clusterSplit, makeCluster, parApply, parCapply, parLapply, parRapply, parSapply,
    splitIndices, stopCluster

### load the updates recommended by the developer
source("http://phylo.wdfiles.com/local--files/biogeobears/cladoRcpp.R") # (needed now that traits model added; source FIRST!)

Loading required package: roxygen2

source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_add_fossils_randomly_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_basics_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_calc_transition_matrices_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_classes_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_detection_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_DNA_cladogenesis_sim_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_extract_Qmat_COOmat_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_generics_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_models_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_on_multiple_trees_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_plots_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_readwrite_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_simulate_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_SSEsim_makePlots_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_SSEsim_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_stochastic_mapping_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_stratified_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_univ_model_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/calc_uppass_probs_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/calc_loglike_sp_v01.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/get_stratified_subbranch_top_downpass_likelihoods_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/runBSM_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/stochastic_map_given_inputs.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/summarize_BSM_tables_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_traits_v1.R") # added traits model
calc_loglike_sp = compiler::cmpfun(calc_loglike_sp_prebyte)    # crucial to fix bug in uppass calculations
calc_independent_likelihoods_on_each_branch = compiler::cmpfun(calc_independent_likelihoods_on_each_branch_prebyte)

Phylogeny file notes from BioGeoBEARS - taken directly from the BioGeoBEARS website

• Must be binary/bifurcating: no polytomies
• No negative branch lengths (e.g. BEAST MCC consensus trees sometimes have negative branchlengths)
• Be careful of very short branches, as BioGeoBEARS will interpret ultrashort branches as direct ancestors
• You can use non-ultrametric trees, but BioGeoBEARS will interpret any tips significantly below the top of the tree as fossils! This is only a good idea if you actually do have fossils in your tree, as in e.g. Wood, Matzke et al. (2013), Systematic Biology.
• The default settings of BioGeoBEARS make sense for trees where the branchlengths are in units of millions of years, and the tree is 1-1000 units tall. If you have a tree with a total height of e.g. 0.00001, you will need to adjust e.g. the max values of d and e, or (simpler) multiply all your branchlengths to get them into reasonable units.
• DON’T USE SPACES IN SPECIES NAMES, USE E.G. “_"

Geography file notes from BioGeoBEARS - taken directly from the BioGeoBEARS website

• This is a PHLYIP-formatted file. This means that in the first line,
  • the 1st number equals the number of rows (species)
  • the 2nd number equals the number of columns (number of areas)
• This is the same format used for C++ LAGRANGE geography files.
• All names in the geography file must match names in the phylogeny file.
• DON’T USE SPACES IN SPECIES NAMES, USE E.G. “_"
• Operational taxonomic units (OTUs) should ideally be phylogenetic lineages, i.e. genetically isolated populations. These may or may not be identical with species. You would NOT want to just use specimens, as each specimen automatically can only live in 1 area, which will typically favor DEC+J models. This is fine if the species/lineages really do live in single areas, but you wouldn’t want to assume this without thinking about it at least. In summary, you should collapse multiple specimens into species/lineages if data indicates they are the same genetic population.

Tree processing for BioGeoBEARS

Taking all of the above into account, we will be using Maximum Clade Credibility (MCC) trees made using our MrBayes output instead of the usual MrBayes consensus trees. Treeannotator is a tool commonly used to make MCC trees from BEAST analyses but it is not compatible with much of the metadata written into MrBayes formatted nexus tree files. In order to generate the MCC trees, it is necessary to perform some preliminary reformatting of tree files, ready for use in Treeannotator. The following cell performs most of this task.

root_path<-"MrBayes/Tree_files/MrBayes_treesets/"
my_files<-list.files(path = root_path, pattern = "*GTRIG.nex.run.{2,3}t")
for(i in 1:length(my_files)){
trees_in<-read.nexus(file = paste(root_path, my_files[i], sep =""))
write.nexus(trees_in[2501:10001], file = paste(root_path,"run",i,"_out.t", sep=''))
}

Bit about how you need to modify the trimmed and cleaned treefiles before treannotator will accept the file

In addition to Treeannotator being incompatible with MrBayes format nexus files, BioGeoBEARS is additionally incompatible with the nexus formatted trees from Treeannotator. BioGeoBEARS also does not use standard phylo class objects in R so we have to convert all of our nexus files to newick format by importing the nexus trees and then exporting them in newick format. The next cell performs this task.

### read in the MCC Cratopus tree from tree annotator
for( i in 1:length(my_files)){
my.nexus.tree<-read.nexus(paste("MrBayes/Tree_files/MrBayes_treesets/run",i,"_out.t.MCC", sep=""))
### read in the list of names
name<-read.csv("Metadata/Multilocus_names.csv")
### read.csv turns text into factors, this gets messy later when plotting
### so make it character data
name<-data.frame(lapply(name, as.character), stringsAsFactors=FALSE)
#my.nexus.tree$tip.label<-name$Alt_label[match(my.nexus.tree$tip.label,name$Name)]
### BioGeoBEARS doesn't like nexus files so we export this as .newick format and reimport it.
write.tree(my.nexus.tree, file=paste("MrBayes/Tree_files/MrBayes_treesets/run",i,"_MCC.newick", sep=""))
}

my_newicks<-list.files(path = root_path, pattern = "*.newick")
teststable = NULL
for(i in my_newicks){
# "trfn" = "tree file name"
trfn <- paste(root_path,i, sep ="")
# Look at the raw Newick file:
moref(trfn)
# Look at your phylogeny:
my.tree <- read.tree(trfn)
### provide a path to the geography file
geogfn <- "Metadata/BioGeoBEARS_islands.txt"
### provide a path to the distance matrix
dist.mat<-"Metadata/BioGeoBEARS_dist.txt"
# Look at the raw geography text file:
moref(geogfn)
# Look at your geographic range data:
tipranges = getranges_from_LagrangePHYLIP(lgdata_fn=geogfn)
tipranges
# Set the maximum number of areas any species may occupy; this cannot be larger
# than the number of areas you set up, but it can be smaller.
max_range_size = 2
#######################################################
# DEC AND DEC+J ANALYSES
#NOTE: The BioGeoBEARS "DEC" model is identical with  the Lagrange DEC model, and should return identical  ML estimates of parameters, and the same  log-likelihoods, for the same datasets. Ancestral state probabilities at nodes will be slightly different, since BioGeoBEARS is reporting the ancestral state probabilities under the global ML model, and Lagrange is reporting ancestral state probabilities after re-optimizing the likelihood after fixing the state at each node. These will be similar, but not identical. See Matzke (2014), Systematic Biology, for discussion. Also see Matzke (2014) for presentation of the DEC+J model.
### Run DEC
#Intitialize a default model (DEC model)
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
# Give BioGeoBEARS the location of the phylogeny Newick file
BioGeoBEARS_run_object$trfn = trfn
# Give BioGeoBEARS the location of the geography text file
BioGeoBEARS_run_object$geogfn = geogfn
# Input the maximum range size
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001    # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE    # set to FALSE for e.g. DEC* model, DEC*+J, etc. (see Massana et al.)
# also search script on "include_null_range" for other places to change
#Speed options and multicore processing if desired
BioGeoBEARS_run_object$speedup = TRUE          # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = TRUE     # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 1
BioGeoBEARS_run_object$force_sparse = FALSE    # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE    # get ancestral states from optim run
# Set up DEC model
# (nothing to do; defaults)
# Look at the BioGeoBEARS_run_object; it's just a list of settings etc.
BioGeoBEARS_run_object
# This contains the model object
BioGeoBEARS_run_object$BioGeoBEARS_model_object
# This table contains the parameters of the model
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table
# Run this to check inputs. Read the error messages if you get them!
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
### These analyses spit out some R.data files so make a subdirectory od /Data to save them in
dir.create("Metadata/BioGeoBEARS_save_files", showWarnings = FALSE)
# For a slow analysis, run once, then set runslow=FALSE to just
# load the saved result.
runslow = TRUE
resfn = "Metadata/BioGeoBEARS_save_files/Cratopus_DEC.Rdata"
if (runslow)
{
  res = bears_optim_run(BioGeoBEARS_run_object)
  res
  save(res, file=resfn)
  resDEC = res
} else {
  # Loads to "res"
  load(resfn)
  resDEC = res
}
### Run DEC+J
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001    # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE    # set to FALSE for e.g. DEC* model, DEC*+J, etc. (see Massana et al.)
# also search script on "include_null_range" for other places to change
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$speedup = TRUE          # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = TRUE     # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 1
BioGeoBEARS_run_object$force_sparse = FALSE    # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE    # get ancestral states from optim run
# Set up DEC+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resDEC$outputs@params_table["d","est"]
estart = resDEC$outputs@params_table["e","est"]
jstart = 0.0001
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# Add j as a free parameter
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
resfn = "Metadata/BioGeoBEARS_save_files/Cratopus_DEC+J.Rdata"
runslow = TRUE
if (runslow)
{
  res = bears_optim_run(BioGeoBEARS_run_object)
  res
  save(res, file=resfn)
  resDECj = res
} else {
  # Loads to "res"
  load(resfn)
  resDECj = res
}
# CALCULATE SUMMARY STATISTICS TO COMPARE
# DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAREALIKE, BAYAREALIKE+J
# REQUIRED READING:
#
# Practical advice / notes / basic principles on statistical model 
#    comparison in general, and in BioGeoBEARS:
# http://phylo.wikidot.com/advice-on-statistical-model-comparison-in-biogeobears
#########################################################################
# Set up empty tables to hold the statistical results
restable = NULL
#######################################################
# Statistics -- DEC vs. DEC+J
#######################################################
# We have to extract the log-likelihood differently, depending on the 
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resDEC)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resDECj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# DEC, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resDEC, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# DEC+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resDECj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# The null hypothesis for a Likelihood Ratio Test (LRT) is that two models
# confer the same likelihood on the data. See: Brian O'Meara's webpage:
# http://www.brianomeara.info/tutorials/aic
# ...for an intro to LRT, AIC, and AICc
rbind(res2, res1)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#write.csv(x = teststable, file = "Metadata/BioGeoBEARS_save_files/teststable.csv")
#######################################################
# PDF plots
#######################################################
pdffn = paste("Metadata/BioGeoBEARS_save_files/Cratopus_DEC+J_",i,".pdf",sep="")
pdf(pdffn, width=6, height=12)
#######################################################
# Plot ancestral states - DEC
#######################################################
#analysis_titletxt ="BioGeoBEARS DEC on Cratopus MCC tree"
# Setup
#results_object = resDEC
#scriptdir = np(system.file("extdata/a_scripts", package="BioGeoBEARS"))
# States
#res2 = plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="text", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
# Pie chart
#plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="pie", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
#######################################################
# Plot ancestral states - DECJ
#######################################################
analysis_titletxt ="BioGeoBEARS DEC+J on Cratopus MCC tree"
# Setup
results_object = resDECj
scriptdir = np(system.file("extdata/a_scripts", package="BioGeoBEARS"))
# States
#res1 = plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="text", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
# Pie chart
plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="pie", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
dev.off()  # Turn off PDF
cmdstr = paste("open ", pdffn, sep="")
system(cmdstr) # Plot it
}

Read 1 item

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Read 59 items

58  12  (A B C D E F G H I J K L)
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A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
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F01_Moh1    000000100000
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G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
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B03_Mau3189 001000000000
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H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
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B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.7514



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

unused control arguments ignored

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max         est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.569469136               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 0.001522257               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.000000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.000000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.000000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.000000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.000000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.000000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.000000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.000000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.000000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.000000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.000000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.000000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.000100000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.000100000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.000100000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.000100000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.000100000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.500000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.100000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.000000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.000000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-152.3922



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 3.75547

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

Parameters or bounds appear to have different scalings.
  This can cause poor performance in optimization. 
  It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.unused control arguments ignored

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type        init    min      max          est                note                                                                  desc
d        free 0.569469136  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.001522257  1e-12  5.00000 5.365722e-01               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.000000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.000000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.000000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.000000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.000000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.000000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.000100000  1e-05  2.99999 3.303034e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.999990000  1e-05  3.00000 2.966970e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.999990000  1e-05  2.00000 1.977980e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.000000000  1e-05  1.00000 9.889899e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.000000000  1e-05  1.00000 9.889899e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.000000000  1e-05  1.00000 9.889899e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.000100000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.000100000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.000100000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.000100000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.000100000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.500000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.100000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.000000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.000000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

(((((((((((A01_Ald3:0.008792710372,((((A07_Ari1:0.001933379197,G02_Csne2:0.0019333792):0.0007324543348,H09_Csn1:0.00266583353):0.001038888909,C05_Dig2:0.003704722434):0.001731933401,B02_Sil4:0.00543665584):0.003356054522):0.001273816663,G01_Ald9:0.01006652703):0.001529188799,((C09_EUR51:0.005209677624,F06_JDN39:0.005209677625):0.004232500074,G10_GGL2:0.009442177718):0.002153538119):0.008515680088,((E04_Mau270:0.009577712127,Reu_punc:0.009577712125):0.009434895361,H05_GRA5:0.01901260749):0.001098788402):0.004862854597,F01_Moh1:0.02497425041):0.06428547782,(((C04_Reu3837:0.02576356468,C06_Reu2000:0.02576356468):0.01151496007,D04_Reu4006:0.03727852472):0.02606884977,E09_Reu268:0.06334737461):0.02591235367):0.02220182236,(B03_Mau3189:0.06577286012,(((C02_Mau3202:0.0247617774,H08_Mau3333:0.0247617774):0.01214925654,H07_Mau3099:0.03691103386):0.01992976707,(D02_Mau3001:0.03252138896,Reu_mur:0.03252138896):0.02431941206):0.008932059095):0.0456886905):0.0255492103,(((A02_Reu3926:0.07941354974,G07_Mau3572:0.07941354974):0.02777902565,(F09_Mau3649:0.01628591503,H01_Reu3824:0.01628591503):0.09090666058):0.02016819301,((((A06_Mau3268:0.02904361861,B04_Reu3461:0.02904361861):0.07684770217,C08_Mau3856:0.1058913208):0.008691532336,(B08_Mau256:0.07831514612,((D09_Reu2041:0.009054266732,H06_Mau3721:0.009054266732):0.05385356462,(E06_Mau3680:0.04940991692,F07_Mau3606:0.04940991695):0.01349791442):0.01540731481):0.03626770708):0.002703101138,(B06_Mau3284:0.1034123735,(D07_Rod286:0.04479963275,F10_Rod164:0.04479963275):0.05861274092):0.01387358077):0.01007481391):0.00964999243):0.008656374614,(A09_Mau3863:0.1264760479,((C10_Reu2580:0.03868300738,G08_Mau3628:0.03868300738):0.07418828548,E10_Reu3072:0.1128712927):0.01360475461):0.01919108812):0.02658906827,((((((A11_Rod56:0.0439500838,H10_Rod124:0.0439500838):0.03770787091,H03_Rod257:0.0816579546):0.02747009297,((B05_Anj4:0.02225832341,G05_Anj3:0.02225832341):0.03689725422,G03_GRA10:0.05915557756):0.04997247011):0.02603140248,(((B01_Dig5:0.006269648043,D06_Pra6:0.006269648049):0.004369730062,B11_Mah1:0.0106393781):0.05607881214,F08_Dig6:0.06671819025):0.06844125982):0.01967118133,((B10_Reu2320:0.01331731059,E07_Mau3707:0.01331731059):0.05610447046,D01_Reu5116:0.06942178095):0.08540885065):0.01008395569,G09_Mau3357:0.1649145874):0.007341616458):0.008576740115,A04_Mad502:0.1808329438):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-217.2799



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.8327739               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 4.9986925               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-153.9452



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.698856

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.8327739  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 4.9986925  1e-12  5.00000 1.722221e+00               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.359260e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.966407e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.977605e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.888025e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.888025e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.888025e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

((((((((((((A01_Ald3:0.008620204365,((((A07_Ari1:0.001897595529,G02_Csne2:0.00189759553):0.0007337235041,H09_Csn1:0.002631319038):0.0009869683464,C05_Dig2:0.00361828738):0.00173778822,B02_Sil4:0.005356075604):0.003264128781):0.00129364962,G01_Ald9:0.00991385397):0.001521490975,((C09_EUR51:0.005145261897,F06_JDN39:0.005145261901):0.004222596269,G10_GGL2:0.009367858154):0.002067486788):0.007557990237,H05_GRA5:0.01899333519):0.0009452637891,(E04_Mau270:0.009437968433,Reu_punc:0.009437968433):0.01050063057):0.004820798484,F01_Moh1:0.02475939748):0.06465776195,(((C04_Reu3837:0.02594153544,C06_Reu2000:0.02594153544):0.01147648309,D04_Reu4006:0.03741801852):0.02636334812,E09_Reu268:0.06378136665):0.02563579275):0.02212111896,(B03_Mau3189:0.06544717463,(((C02_Mau3202:0.02473101608,H08_Mau3333:0.02473101608):0.01206892012,H07_Mau3099:0.03679993626):0.01987510044,(D02_Mau3001:0.03241800067,Reu_mur:0.03241800066):0.02425703604):0.008772137839):0.04609110378):0.02580853156,(((A02_Reu3926:0.07959985273,G07_Mau3572:0.07959985273):0.0277796786,(F09_Mau3649:0.01637565274,H01_Reu3824:0.01637565274):0.0910038787):0.02029271233,((((A06_Mau3268:0.02919322308,B04_Reu3461:0.02919322308):0.07701141721,C08_Mau3856:0.1062046405):0.008850497169,(B08_Mau256:0.07818430908,((D09_Reu2041:0.009050788002,H06_Mau3721:0.009050788002):0.05383685771,(E06_Mau3680:0.04933553664,F07_Mau3606:0.04933553663):0.01355210901):0.01529666339):0.03687082839):0.00258679689,(B06_Mau3284:0.1036592421,(D07_Rod286:0.04484378713,F10_Rod164:0.04484378713):0.05881545505):0.01398269218):0.01003030939):0.009674566249):0.00874063523,(A09_Mau3863:0.1265488019,((C10_Reu2580:0.03883775295,G08_Mau3628:0.03883775295):0.07439798227,E10_Reu3072:0.1132357355):0.01331306685):0.01953864299):0.02682669326,((((((A11_Rod56:0.04439128664,H10_Rod124:0.04439128664):0.03770803919,H03_Rod257:0.08209932559):0.02745526706,((B05_Anj4:0.02205474942,G05_Anj3:0.02205474942):0.03738162338,G03_GRA10:0.05943637289):0.05011822001):0.02625745353,(((B01_Dig5:0.006282334018,D06_Pra6:0.006282334013):0.004367294293,B11_Mah1:0.01064962828):0.05597352275,F08_Dig6:0.06662315107):0.0691888953):0.0195738389,((B10_Reu2320:0.01327117157,E07_Mau3707:0.01327117157):0.05615000557,D01_Reu5116:0.06942117715):0.08596470815):0.0100573546,G09_Mau3357:0.16544324):0.007470898485):0.008666152788,A04_Mad502:0.1815802906):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.7684



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.6974381               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 2.0633546               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-150.3111



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.314574

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.6974381  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 2.0633546  1e-12  5.00000 1.000000e-12               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.305621e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.966944e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.977963e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.889813e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.889813e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.889813e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

(((((((((((A01_Ald3:0.008700284599,((((A07_Ari1:0.001953308951,G02_Csne2:0.001953308955):0.0007272353125,H09_Csn1:0.002680544263):0.0009845947979,C05_Dig2:0.003665139064):0.001734461648,B02_Sil4:0.005399600706):0.003300683898):0.001331860298,G01_Ald9:0.01003214491):0.001515695538,((C09_EUR51:0.005161141028,F06_JDN39:0.005161141024):0.004219068828,G10_GGL2:0.009380209833):0.002167630592):0.008483660929,((E04_Mau270:0.009539002996,Reu_punc:0.009539002998):0.009328815111,H05_GRA5:0.01886781814):0.001163683286):0.005075154761,F01_Moh1:0.02510665619):0.06421459464,(((C04_Reu3837:0.02582778684,C06_Reu2000:0.02582778684):0.01143375584,D04_Reu4006:0.03726154258):0.02636878585,E09_Reu268:0.06363032842):0.02569092243):0.02230932379,(B03_Mau3189:0.0657665488,(((C02_Mau3202:0.02483564405,H08_Mau3333:0.02483564404):0.01221334566,H07_Mau3099:0.0370489897):0.01982641084,(D02_Mau3001:0.03260004275,Reu_mur:0.03260004274):0.02427535777):0.00889114828):0.04586402577):0.02570871832,(((A02_Reu3926:0.07947319239,G07_Mau3572:0.07947319239):0.02791252622,(F09_Mau3649:0.01615788217,H01_Reu3824:0.01615788217):0.09122783655):0.0202382428,((((A06_Mau3268:0.02905644572,B04_Reu3461:0.02905644572):0.07662648488,C08_Mau3856:0.1056829306):0.009042928126,(B08_Mau256:0.07835054669,((D09_Reu2041:0.00915046121,H06_Mau3721:0.00915046121):0.05373961031,(E06_Mau3680:0.04924557938,F07_Mau3606:0.04924557937):0.01364449211):0.0154604752):0.03637531203):0.002731086651,(B06_Mau3284:0.1036111402,(D07_Rod286:0.0450250801,F10_Rod164:0.0450250801):0.05858606006):0.01384580527):0.01016701616):0.009715331422):0.00878308134,(A09_Mau3863:0.126591129,((C10_Reu2580:0.03871805266,G08_Mau3628:0.03871805266):0.07479989507,E10_Reu3072:0.1135179477):0.01307318158):0.01953124505):0.02692923403,((((((A11_Rod56:0.04443493278,H10_Rod124:0.04443493278):0.03762623582,H03_Rod257:0.08206116856):0.02744795873,((B05_Anj4:0.0221838449,G05_Anj3:0.0221838449):0.03740263723,G03_GRA10:0.05958648222):0.0499226452):0.0261806666,(((B01_Dig5:0.006303682375,D06_Pra6:0.006303682373):0.004318607965,B11_Mah1:0.01062229033):0.05617942412,F08_Dig6:0.06680171457):0.06888807945):0.01986881797,((B10_Reu2320:0.01332911787,E07_Mau3707:0.01332911787):0.05629927251,D01_Reu5116:0.06962839038):0.08593022171):0.01013979339,G09_Mau3357:0.1656984049):0.007353202989):0.008530391855,A04_Mad502:0.1815819998):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-217.1541



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.7132598               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 1.9934791               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-150.6819



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.299612

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.7132598  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 1.9934791  1e-12  5.00000 1.293619e+00               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.384661e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.966153e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.977436e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.887178e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.887178e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.887178e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Couldn't get a file descriptor referring to the console
Read 1 item

((((((((((((A01_Ald3:0.008813147632,((((A07_Ari1:0.001976229597,G02_Csne2:0.0019762296):0.0007303710702,H09_Csn1:0.002706600669):0.001012902649,C05_Dig2:0.003719503318):0.001777969149,B02_Sil4:0.005497472463):0.003315675167):0.001310114986,G01_Ald9:0.01012326258):0.001507997081,((C09_EUR51:0.005215296094,F06_JDN39:0.005215296097):0.00428870441,G10_GGL2:0.009504000554):0.002127259186):0.00751648632,H05_GRA5:0.01914774604):0.0009804727654,(E04_Mau270:0.009553682883,Reu_punc:0.009553682884):0.01057453595):0.004942927632,F01_Moh1:0.02507114638):0.06459088754,(((C04_Reu3837:0.02571797197,C06_Reu2000:0.02571797198):0.01146567689,D04_Reu4006:0.03718364899):0.02632636317,E09_Reu268:0.06351001217):0.02615202174):0.02223920036,(B03_Mau3189:0.06574145986,(((C02_Mau3202:0.0248797195,H08_Mau3333:0.02487971951):0.01209637428,H07_Mau3099:0.03697609378):0.01985611952,(D02_Mau3001:0.03259468808,Reu_mur:0.03259468808):0.02423752514):0.008909246591):0.04615977441):0.02579316725,(((A02_Reu3926:0.07998952872,G07_Mau3572:0.07998952872):0.02767396819,(F09_Mau3649:0.01649071962,H01_Reu3824:0.01649071962):0.09117277722):0.02037558902,((((A06_Mau3268:0.02942220301,B04_Reu3461:0.02942220301):0.07683819626,C08_Mau3856:0.1062603994):0.008938861822,(B08_Mau256:0.07839980841,((D09_Reu2041:0.009137980308,H06_Mau3721:0.009137980308):0.05379916105,(E06_Mau3680:0.04925794297,F07_Mau3606:0.04925794304):0.01367919829):0.0154626671):0.03679945255):0.002804346713,(B06_Mau3284:0.1038593352,(D07_Rod286:0.04496206214,F10_Rod164:0.04496206214):0.05889727327):0.01414427241):0.01003547822):0.009655315594):0.008987008303,(A09_Mau3863:0.1274928408,((C10_Reu2580:0.0388436246,G08_Mau3628:0.0388436246):0.07494298374,E10_Reu3072:0.1137866077):0.01370623298):0.01918856859):0.02664599906,((((((A11_Rod56:0.04435418992,H10_Rod124:0.04435418992):0.03769387422,H03_Rod257:0.08204806415):0.02747267396,((B05_Anj4:0.02210862238,G05_Anj3:0.02210862238):0.03730688797,G03_GRA10:0.05941551028):0.05010522763):0.02635763979,(((B01_Dig5:0.006364308082,D06_Pra6:0.00636430808):0.004327595071,B11_Mah1:0.01069190314):0.05619487768,F08_Dig6:0.06688678086):0.06899159696):0.01992886096,((B10_Reu2320:0.01330696656,E07_Mau3707:0.01330696656):0.05620824015,D01_Reu5116:0.06951520666):0.08629203206):0.009999598291,G09_Mau3357:0.1658068374):0.007520571814):0.008609754352,A04_Mad502:0.1819371628):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.6083



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.6673786               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 1.5764627               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-150.1188



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.197684

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.6673786  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 1.5764627  1e-12  5.00000 1.108837e+00               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 2.949559e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.970504e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.980336e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.901681e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.901681e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.901681e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

((((((((((((A01_Ald3:0.008717882127,((((A07_Ari1:0.001967840647,G02_Csne2:0.001967840644):0.0007102854424,H09_Csn1:0.002678126084):0.001005269798,C05_Dig2:0.00368339588):0.001731497168,B02_Sil4:0.005414893058):0.003302989095):0.001310740776,G01_Ald9:0.01002862289):0.001553450352,((C09_EUR51:0.005230355432,F06_JDN39:0.005230355434):0.004199077042,G10_GGL2:0.009429432451):0.002152640792):0.007511825678,H05_GRA5:0.01909389892):0.0009370332457,(E04_Mau270:0.009426887368,Reu_punc:0.009426887369):0.01060404485):0.004853792644,F01_Moh1:0.02488472487):0.06429646477,(((C04_Reu3837:0.02585956411,C06_Reu2000:0.02585956411):0.01143103954,D04_Reu4006:0.0372906036):0.02634495409,E09_Reu268:0.06363555774):0.02554563187):0.02222765827,(B03_Mau3189:0.06537895364,(((C02_Mau3202:0.02474193041,H08_Mau3333:0.02474193041):0.01225213479,H07_Mau3099:0.03699406524):0.01960645737,(D02_Mau3001:0.03222260289,Reu_mur:0.03222260289):0.02437791979):0.008778430989):0.04602989418):0.02576376517,(((A02_Reu3926:0.07951539052,G07_Mau3572:0.07951539052):0.02761589681,(F09_Mau3649:0.01632181546,H01_Reu3824:0.01632181546):0.09080947203):0.02033789842,((((A06_Mau3268:0.02928918021,B04_Reu3461:0.02928918021):0.0763282741,C08_Mau3856:0.1056174544):0.009054668489,(B08_Mau256:0.07827605256,((D09_Reu2041:0.009007268069,H06_Mau3721:0.009007268069):0.05394962501,(E06_Mau3680:0.04932084553,F07_Mau3606:0.0493208455):0.01363604756):0.01531915949):0.03639607015):0.002658692832,(B06_Mau3284:0.1032636596,(D07_Rod286:0.0448268835,F10_Rod164:0.0448268835):0.05843677586):0.01406715622):0.01013837033):0.009703427207):0.008810580396,(A09_Mau3863:0.1266576123,((C10_Reu2580:0.03868112934,G08_Mau3628:0.03868112934):0.07437013833,E10_Reu3072:0.1130512675):0.01360634464):0.01932558114):0.02687459721,((((((A11_Rod56:0.04414652617,H10_Rod124:0.04414652617):0.03781631422,H03_Rod257:0.08196284054):0.02756010973,((B05_Anj4:0.02210274478,G05_Anj3:0.02210274478):0.03730832551,G03_GRA10:0.05941107023):0.05011187994):0.02606948539,(((B01_Dig5:0.006239925371,D06_Pra6:0.006239925374):0.004327760166,B11_Mah1:0.01056768553):0.05630919387,F08_Dig6:0.0668768795):0.06871555614):0.0199619073,((B10_Reu2320:0.01339548159,E07_Mau3707:0.01339548159):0.05615628164,D01_Reu5116:0.0695517633):0.08600257971):0.009938965166,G09_Mau3357:0.1654933081):0.007364482456):0.008678669675,A04_Mad502:0.1815364605):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.7125



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.6880551               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 1.8612448               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-150.1863



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.269803

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.6880551  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 1.8612448  1e-12  5.00000 1.000000e-12               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.304789e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.966952e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.977968e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.889840e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.889840e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.889840e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

((((((((((((A01_Ald3:0.008626403163,((((A07_Ari1:0.001920871955,G02_Csne2:0.001920871954):0.0007088571386,H09_Csn1:0.002629729099):0.001017724792,C05_Dig2:0.003647453896):0.001723568438,B02_Sil4:0.005371022342):0.003255380824):0.001265427596,G01_Ald9:0.009891830729):0.001510962279,((C09_EUR51:0.00517429705,F06_JDN39:0.005174297047):0.004193235237,G10_GGL2:0.009367532306):0.002035260733):0.007507880682,H05_GRA5:0.01891067368):0.0009448854115,(E04_Mau270:0.009407800249,Reu_punc:0.009407800247):0.01044775887):0.004877591953,F01_Moh1:0.02473315109):0.06457410407,(((C04_Reu3837:0.02574418104,C06_Reu2000:0.02574418104):0.01144594319,D04_Reu4006:0.03719012423):0.0262423082,E09_Reu268:0.0634324325):0.02587482265):0.02227985701,(B03_Mau3189:0.0655575847,(((C02_Mau3202:0.02471017522,H08_Mau3333:0.02471017523):0.01226722128,H07_Mau3099:0.03697739654):0.01984161508,(D02_Mau3001:0.03257009523,Reu_mur:0.03257009523):0.02424891636):0.008738573162):0.0460295274):0.02588496806,(((A02_Reu3926:0.07965646606,G07_Mau3572:0.07965646606):0.02768126626,(F09_Mau3649:0.01636286905,H01_Reu3824:0.01636286905):0.09097486359):0.02039611583,((((A06_Mau3268:0.02921295802,B04_Reu3461:0.02921295802):0.07673038357,C08_Mau3856:0.1059433416):0.008893797344,(B08_Mau256:0.07833486695,((D09_Reu2041:0.009001825293,H06_Mau3721:0.009001825293):0.05384605243,(E06_Mau3680:0.04937373372,F07_Mau3606:0.04937373369):0.01347414397):0.01548698928):0.03650227207):0.002735266818,(B06_Mau3284:0.1036998226,(D07_Rod286:0.04472380228,F10_Rod164:0.04472380228):0.05897602033):0.01387258323):0.01016144245):0.009738231925):0.008839281892,(A09_Mau3863:0.1264936217,((C10_Reu2580:0.03867047102,G08_Mau3628:0.03867047102):0.07415121525,E10_Reu3072:0.1128216864):0.01367193528):0.01981774055):0.02698862563,((((((A11_Rod56:0.04435941919,H10_Rod124:0.04435941919):0.03768876964,H03_Rod257:0.08204818875):0.02746721892,((B05_Anj4:0.02207843987,G05_Anj3:0.02207843987):0.0372361947,G03_GRA10:0.05931463458):0.05020077306):0.02620130033,(((B01_Dig5:0.006295912896,D06_Pra6:0.006295912882):0.00433655771,B11_Mah1:0.0106324706):0.0563058145,F08_Dig6:0.06693828515):0.06877842297):0.01988614626,((B10_Reu2320:0.01336939219,E07_Mau3707:0.01336939219):0.05644778122,D01_Reu5116:0.06981717343):0.08578568066):0.01027871914,G09_Mau3357:0.1658815733):0.007418414345):0.008663404657,A04_Mad502:0.1819633924):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.7628



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.2503015               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 4.8815300               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-161.2423



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.688556

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.2503015  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 4.8815300  1e-12  5.00000 1.667050e+00               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.568886e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.964311e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.976207e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.881037e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.881037e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.881037e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

((((((((((((A01_Ald3:0.008733808737,((((A07_Ari1:0.001934656978,G02_Csne2:0.001934656984):0.0007321075601,H09_Csn1:0.002666764543):0.0009520110381,C05_Dig2:0.003618775576):0.001787447171,B02_Sil4:0.005406222741):0.003327585978):0.001347405575,G01_Ald9:0.01008121429):0.001535896294,((C09_EUR51:0.005214805829,F06_JDN39:0.005214805831):0.004276457031,G10_GGL2:0.009491262858):0.002125847745):0.007550721855,H05_GRA5:0.01916783245):0.0009406112558,(E04_Mau270:0.009499229848,Reu_punc:0.009499229848):0.01060921395):0.004829825259,F01_Moh1:0.024938269):0.06460165057,(((C04_Reu3837:0.02593461967,C06_Reu2000:0.02593461967):0.01156230339,D04_Reu4006:0.03749692304):0.02627834936,E09_Reu268:0.06377527242):0.02576464714):0.02221673071,(B03_Mau3189:0.06551929022,(((C02_Mau3202:0.02468939824,H08_Mau3333:0.02468939824):0.01205257352,H07_Mau3099:0.03674197169):0.01987288085,(D02_Mau3001:0.03228815105,Reu_mur:0.03228815105):0.02432670152):0.008904437685):0.04623735994):0.02570824632,(((A02_Reu3926:0.07960621754,G07_Mau3572:0.07960621754):0.02771591345,(F09_Mau3649:0.01637680829,H01_Reu3824:0.01637680829):0.09094532274):0.02044304848,((((A06_Mau3268:0.02925496779,B04_Reu3461:0.02925496779):0.0768628299,C08_Mau3856:0.1061177976):0.008994533992,(B08_Mau256:0.07848196296,((D09_Reu2041:0.009110329928,H06_Mau3721:0.009110329928):0.05380893341,(E06_Mau3680:0.04931864816,F07_Mau3606:0.04931864814):0.01360061507):0.01556269976):0.03663036868):0.002668750414,(B06_Mau3284:0.1037906999,(D07_Rod286:0.04492277285,F10_Rod164:0.04492277285):0.05886792707):0.01399038214):0.009984097576):0.009699716894):0.008774880183,(A09_Mau3863:0.1267768236,((C10_Reu2580:0.038703752,G08_Mau3628:0.038703752):0.07436366093,E10_Reu3072:0.113067413):0.01370941115):0.01946295253):0.0267627015,((((((A11_Rod56:0.0444634524,H10_Rod124:0.0444634524):0.03722249963,H03_Rod257:0.08168595206):0.02762476596,((B05_Anj4:0.02220438657,G05_Anj3:0.02220438657):0.03726567129,G03_GRA10:0.0594700579):0.04984066013):0.02635530018,(((B01_Dig5:0.006301715029,D06_Pra6:0.00630171503):0.004371745209,B11_Mah1:0.01067346022):0.05587618804,F08_Dig6:0.06654964825):0.06911636974):0.01982229955,((B10_Reu2320:0.01329910794,E07_Mau3707:0.01329910794):0.05634124847,D01_Reu5116:0.0696403564):0.08584796115):0.01017734694,G09_Mau3357:0.1656656647):0.007336813538):0.00862463752,A04_Mad502:0.1816271161):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.6566



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.7425363               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 4.2740616               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-152.4666



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.630841

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.7425363  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 4.2740616  1e-12  5.00000 3.765751e+00               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.331289e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.966687e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.977791e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.888957e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.888957e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.888957e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

((((((((((((A01_Ald3:0.008730302645,((((A07_Ari1:0.001934655522,G02_Csne2:0.001934655524):0.0007529938972,H09_Csn1:0.00268764942):0.0009997016558,C05_Dig2:0.00368735108):0.001729705296,B02_Sil4:0.005417056383):0.003313246272):0.001274186637,G01_Ald9:0.01000448928):0.001536895575,((C09_EUR51:0.005151700974,F06_JDN39:0.005151700972):0.004287293592,G10_GGL2:0.009438994553):0.002102390275):0.007605183351,H05_GRA5:0.01914656822):0.0009219742814,(E04_Mau270:0.009508915029,Reu_punc:0.009508915031):0.01055962746):0.00484905962,F01_Moh1:0.02491760208):0.06479791331,(((C04_Reu3837:0.02591727396,C06_Reu2000:0.02591727396):0.01146607431,D04_Reu4006:0.0373833483):0.0266171658,E09_Reu268:0.06400051417):0.02571500135):0.02213898114,(B03_Mau3189:0.06581736148,(((C02_Mau3202:0.0249214979,H08_Mau3333:0.0249214979):0.01206049342,H07_Mau3099:0.03698199127):0.0197990637,(D02_Mau3001:0.03238366084,Reu_mur:0.03238366084):0.02439739414):0.009036306458):0.04603713511):0.0258281239,(((A02_Reu3926:0.07972483484,G07_Mau3572:0.07972483484):0.02784927681,(F09_Mau3649:0.01632992288,H01_Reu3824:0.01632992288):0.09124418895):0.0203551789,((((A06_Mau3268:0.02924140759,B04_Reu3461:0.02924140759):0.0771265004,C08_Mau3856:0.1063679075):0.008874280137,(B08_Mau256:0.07847827673,((D09_Reu2041:0.009075081093,H06_Mau3721:0.009075081093):0.05384845283,(E06_Mau3680:0.04911711465,F07_Mau3606:0.04911711462):0.01380641925):0.01555474285):0.03676391127):0.002666284405,(B06_Mau3284:0.1038579235,(D07_Rod286:0.04492124552,F10_Rod164:0.04492124552):0.05893667833):0.01405054857):0.01002081815):0.009753329976):0.00876618614,(A09_Mau3863:0.1270106968,((C10_Reu2580:0.0387611357,G08_Mau3628:0.0387611357):0.07485477442,E10_Reu3072:0.1136159099):0.01339478719):0.01943810935):0.02678474825,((((((A11_Rod56:0.04461000801,H10_Rod124:0.04461000801):0.03765188331,H03_Rod257:0.08226189139):0.02731104591,((B05_Anj4:0.02212183437,G05_Anj3:0.02212183437):0.0373897774,G03_GRA10:0.05951161178):0.05006132542):0.0263171016,(((B01_Dig5:0.006312767483,D06_Pra6:0.006312767477):0.004347727431,B11_Mah1:0.0106604949):0.05620718526,F08_Dig6:0.06686768022):0.06902235871):0.01977444716,((B10_Reu2320:0.01340943447,E07_Mau3707:0.01340943447):0.05628468698,D01_Reu5116:0.06969412142):0.08597036429):0.01026621113,G09_Mau3357:0.1659306975):0.007302857865):0.008753109838,A04_Mad502:0.1819866647):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-216.7069



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.6694012               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 1.6390200               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-150.1686



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.214584

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.6694012  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 1.6390200  1e-12  5.00000 5.122884e-01               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.363628e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.966364e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.977576e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.887879e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.887879e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.887879e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console
Read 1 item

(((((((((((A01_Ald3:0.008660305845,((((A07_Ari1:0.001923031335,G02_Csne2:0.001923031334):0.000717616741,H09_Csn1:0.002640648081):0.001026884589,C05_Dig2:0.003667532669):0.001705493863,B02_Sil4:0.005373026524):0.003287279299):0.001339014376,G01_Ald9:0.009999320207):0.001553970588,((C09_EUR51:0.005206449676,F06_JDN39:0.005206449678):0.004272630188,G10_GGL2:0.009479079849):0.002074210931):0.008530782895,((E04_Mau270:0.009514214978,Reu_punc:0.009514214979):0.009415613045,H05_GRA5:0.018929828):0.001154245695):0.004894974116,F01_Moh1:0.02497904785):0.06409990904,(((C04_Reu3837:0.02574185219,C06_Reu2000:0.02574185219):0.01141239366,D04_Reu4006:0.03715424584):0.02625778962,E09_Reu268:0.06341203538):0.0256669214):0.02231483951,(B03_Mau3189:0.06530142763,(((C02_Mau3202:0.02468158978,H08_Mau3333:0.02468158978):0.01207854529,H07_Mau3099:0.0367601351):0.01965813248,(D02_Mau3001:0.03231456092,Reu_mur:0.03231456092):0.02410370655):0.008883160142):0.04609236877):0.02575251777,(((A02_Reu3926:0.07956051099,G07_Mau3572:0.07956051099):0.02770417418,(F09_Mau3649:0.01626697072,H01_Reu3824:0.01626697072):0.09099771445):0.02026867327,((((A06_Mau3268:0.02910430548,B04_Reu3461:0.02910430548):0.07679297439,C08_Mau3856:0.1058972804):0.008874216373,(B08_Mau256:0.07824799973,((D09_Reu2041:0.009163214822,H06_Mau3721:0.009163214822):0.05371081555,(E06_Mau3680:0.04928587932,F07_Mau3606:0.04928587926):0.01358815113):0.01537396933):0.0365234964):0.002703847518,(B06_Mau3284:0.1036429785,(D07_Rod286:0.0450019024,F10_Rod164:0.0450019024):0.05864107593):0.01383236546):0.01005801482):0.009612955606):0.008892411415,(A09_Mau3863:0.1267183108,((C10_Reu2580:0.03879039859,G08_Mau3628:0.03879039859):0.07425574403,E10_Reu3072:0.1130461428):0.01367216887):0.0193204141):0.02660717709,((((((A11_Rod56:0.04437170404,H10_Rod124:0.04437170404):0.03757333444,H03_Rod257:0.0819450385):0.02738511297,((B05_Anj4:0.02208894611,G05_Anj3:0.02208894611):0.03746727663,G03_GRA10:0.05955622267):0.04977392877):0.02619823223,(((B01_Dig5:0.006239848295,D06_Pra6:0.006239848284):0.004295120626,B11_Mah1:0.0105349689):0.05601031942,F08_Dig6:0.06654528832):0.06898309535):0.01961027453,((B10_Reu2320:0.01330276008,E07_Mau3707:0.01330276008):0.05626312436,D01_Reu5116:0.06956588442):0.08557277352):0.01011096366,G09_Mau3357:0.1652496222):0.007396280882):0.008722580581,A04_Mad502:0.1813684829):0;

Read 59 items

58  12  (A B C D E F G H I J K L)
A04_Mad502  100000000000
B04_Reu3461 010000000000
A01_Ald3    000000000010
G01_Ald9    000000000010
A07_Ari1    000000000001
G02_Csne2   000000000001
H09_Csn1    000000000001
G10_GGL2    000001000000
C09_EUR51   000010000000
F06_JDN39   000000000100
B02_Sil4    000000000001
C05_Dig2    000000000001
H05_GRA5    000000001000
E04_Mau270  001000000000
F01_Moh1    000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256  001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268  010000000000
A11_Rod56   000100000000
H10_Rod124  000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164  000100000000
D07_Rod286  000100000000
B01_Dig5    000000000001
D06_Pra6    000000000001
B11_Mah1    000000000001
F08_Dig6    000000000001
B05_Anj4    000000010000
G05_Anj3    000000010000
G03_GRA10   000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257  000100000000
Reu_mur 010000000000
Reu_punc    010000000000
A06_Mau3268 001000000000

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1 1


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-217.2013



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 0

$lbratio
[1] 0

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type    init    min      max       est                note                                                                  desc
d        free 0.01000  1e-12  5.00000 0.8202114               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 0.01000  1e-12  5.00000 5.0000000               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.00000  1e-12  5.00000 0.0000000               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.00000  1e-12  1.00000 1.0000000 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.00000 -1e+01 10.00000 0.0000000               works                               exponent on distance (modifies d, j, a)
n       fixed 0.00000 -1e+01 10.00000 0.0000000               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.00000 -1e+01 10.00000 1.0000000               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.00000 -1e+01 10.00000 0.0000000               works        anagenesis: exponent on extinction risk with area (modifies e)
j       fixed 0.00000  1e-05  2.99999 0.0000000               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.99999  1e-05  3.00000 3.0000000               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.99999  1e-05  2.00000 2.0000000               works                                                     cladogenesis: y+s
y     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.00000  1e-05  1.00000 1.0000000               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.00010  1e-04  0.99990 0.0001000               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.50000  1e-04  0.99990 0.5000000                  no                       root: controls range size probabilities of root
mf      fixed 0.10000  5e-03  0.99500 0.1000000                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.00000  5e-03  0.99500 1.0000000                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.00000  5e-03  0.99500 0.0000000                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items

Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000


NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().

calc_loglike_for_optim() on initial parameters loglike=-153.9184



Calculation of likelihood on initial parameters: successful.

Now starting Maximum Likelihood (ML) parameter optimization with optimx()...



Printing any warnings() that occurred during calc_loglike_for_optim():

NULL


Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.

$lpratio
[1] 4.69897

$lbratio
[1] 0.2218516

Maximizing -- use negfn and neggr

This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:

Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:

BioGeoBEARS_model_object =

An object of class "BioGeoBEARS_model"
Slot "params_table":
         type      init    min      max          est                note                                                                  desc
d        free 0.8202114  1e-12  5.00000 1.000000e-12               works                     anagenesis: rate of 'dispersal' (range expansion)
e        free 5.0000000  1e-12  5.00000 6.826476e-01               works                  anagenesis: rate of 'extinction' (range contraction)
a       fixed 0.0000000  1e-12  5.00000 0.000000e+00               works       anagenesis: rate of range-switching (i.e. for a standard char.)
b       fixed 1.0000000  1e-12  1.00000 1.000000e+00 non-stratified only                                anagenesis: exponent on branch lengths
x       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                               exponent on distance (modifies d, j, a)
n       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works                 exponent on environmental distance (modifies d, j, a)
w       fixed 1.0000000 -1e+01 10.00000 1.000000e+00               works           exponent on manual dispersal multipliers (modifies d, j, a)
u       fixed 0.0000000 -1e+01 10.00000 0.000000e+00               works        anagenesis: exponent on extinction risk with area (modifies e)
j        free 0.0001000  1e-05  2.99999 3.299617e-02               works             cladogenesis: relative per-event weight of jump dispersal
ysv       3-j 2.9999900  1e-05  3.00000 2.967004e+00               works                                                   cladogenesis: y+s+v
ys    ysv*2/3 1.9999900  1e-05  2.00000 1.978003e+00               works                                                     cladogenesis: y+s
y     ysv*1/3 1.0000000  1e-05  1.00000 9.890013e-01               works   cladogenesis: relative per-event weight of sympatry (range-copying)
s     ysv*1/3 1.0000000  1e-05  1.00000 9.890013e-01               works          cladogenesis: relative per-event weight of subset speciation
v     ysv*1/3 1.0000000  1e-05  1.00000 9.890013e-01               works       cladogenesis: relative per-event weight of vicariant speciation
mx01    fixed 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01j    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01y    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01s    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01v    mx01 0.0001000  1e-04  0.99990 1.000000e-04               works                 cladogenesis: controls range size of smaller daughter
mx01r   fixed 0.5000000  1e-04  0.99990 5.000000e-01                  no                       root: controls range size probabilities of root
mf      fixed 0.1000000  5e-03  0.99500 1.000000e-01                 yes                      mean frequency of truly sampling OTU of interest
dp      fixed 1.0000000  5e-03  0.99500 1.000000e+00                 yes              detection probability per true sample of OTU of interest
fdp     fixed 0.0000000  5e-03  0.99500 0.000000e+00                 yes false detection of OTU probability per true taphonomic control sample



...successful.


Uppass starting for marginal ancestral states estimation!

Uppass completed for marginal ancestral states estimation!

NOTE: multiple states tied

Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.

Couldn't get a file descriptor referring to the console

write.table(unlist_df(teststable), file="Metadata/BioGeoBEARS_save_files/Teststable.txt", quote=FALSE, sep="\t")

Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE,  : 
  arguments imply differing number of rows: 0, 10